Machine Learning (ML) Data: Best Machine Learning Datasets & Databases
- Overview
- Datasets
- Providers
- Use Cases
- Attributes
- Guide
- FAQ
- Overview
- Datasets
- Providers
- Use Cases
- Attributes
- Guide
- FAQ
What is Machine Learning (ML) Data?
Machine Learning (ML) data is the information used to train and develop machine learning models. It consists of examples or instances, often represented as numerical values or features. On this page, you’ll find the best data sources for various types of machine learning data.
Best Machine Learning (ML) Databases & Datasets
Here is our curated selection of top Machine Learning (ML) Data sources. We focus on key factors such as data reliability, accuracy, and flexibility to meet diverse use-case requirements. These datasets are provided by trusted providers known for delivering high-quality, up-to-date information.
Machine Learning (ML) Data | 800M+ B2B Profiles | AI-Ready for Deep Learning (DL), NLP & LLM Training
Global Tailored Web Data | AI Training Data | Machine Learning (ML) Data | Tailored Web Data
Acoustic Guitar Dataset for AI-Generated Music (Machine Learning (ML) Data)
2M+ Architecture Images | AI Training Data | Machine Learning (ML) data | Object & Scene Detection | Global Coverage
Gesture Recognition Data |10,000 ID | Computer Vision Data| AI Training Data | Machine Learning (ML) Data
AI & ML Training Data | Artificial Intelligence (AI) | Machine Learning (ML) Datasets | Deep Learning Datasets | Easy to Integrate | Free Sample
Large Language Model (LLM) Data | Machine Learning (ML) Data | AI Training Data (RAG) for 1M+ Global Grocery, Restaurant, and Retail Stores
FileMarket | 20,000 Voice Memos | Multilingual Training Data for Conversational AI | Machine Learning (ML) Data
Rockabilly Dataset for AI-Generated Music (Machine Learning (ML) Data)
20K+ Parking Lots Images | AI Training Data | Machine Learning (ML) data | Object & Scene Detection | Global Coverage
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Top Machine Learning (ML) Data Providers & Companies
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Rightsify
CoverageUSAUK+247GCX by Rightsify provides copyright cleared music datasets for ML and generative AI music projects. We offer millions of hours of music that is available for training and commercial use. All datasets include detailed metadata on the music included in the datasets.Ethical AIClean Data -
Nexdata
CoverageUSAUK+184Founded in 2011, Nexdata has grown to be a globally renowned AI training data service company. Nexdata owns an extensive library of off-the-shelf datasets and provides flexible data collection, annotation and curation services.Volume1M Hours Speech, 800TB ImageAccuracyAbove 95%CopyrightCollected with Consent -
Data Seeds
CoverageUSAUK+248VImageDatasets has been an excellent partner for our image-based AI training needs. Its professional-grade imagery and robust metadata make it a go-to resource for developing state-of-the-art models in photography-focused domains. We recommend their services for AI developers looking for rich, high-quality datasets that come with comprehensive metadata.
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Pixta AI
CoverageUSAUK+247VWe collaborated with Pixta on an AI project. Pixta surprised us with great labelling and annotation services. Pixta Team has a high standard for the services and always double checks with us during the project to ensure alignment. Moreover, Pixta has provided licenced images, even human images, so we have no worries about the legal issue. Pixta is our first-choice partner for all AI projects.
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FileMarket
CoverageUSAUK+248Our platform engages communities to gather hard-to-obtain datasets. By connecting companies with our users, we collect unique data crucial for cutting-edge research. Make a request, and we'll collect non-existent, fully customizable datasets tailored to your needs.GDPRCompliant100%Verified Data5+Data Types -
TagX
CoverageUSAUK+248VWe requested data in the form of various forms and documents associated with Tax and Bookkeeping that we plan to use to developing a digital document processing solution based on artificial intelligence.
Popular Use Cases for Machine Learning (ML) Data
Machine Learning (ML) Data is essential for a wide range of business applications, offering valuable insights and driving opportunities across industries. Below, we have highlighted the most significant use cases for Machine Learning (ML) Data.
Main Attributes of Machine Learning (ML) Data
Below, we outline the most popular attributes associated with this type of data—features that data buyers are actively seeking to meet their needs.
| Attribute | Type | Description | Action |
|---|---|---|---|
| Float | The latitude of a point on earth's surface. Commonly abbreviated as "lat". | View 93 datasets | |
| String | The name of a city. | View 91 datasets | |
| String | The address of a company or contact (street name, number, zip code, city, county, country). | View 84 datasets | |
| String | The postal code of an address. | View 82 datasets | |
| Float | The longitude of a point on earth's surface. Commonly abbreviated as "long". | View 76 datasets | |
| String | The name of a country. | View 67 datasets |
Examples of Machine Learning (ML) Data
Examples of ML data include text documents, images, audio recordings, sensor data, and customer behavior data. Machine learning data is part of AI training data and is used to make predictions, classify data, recognize patterns, and automate decision-making processes.
What are the Different Types of Machine Learning (ML) Training Data?
- Textual Data: Used in natural language processing (NLP) tasks like sentiment analysis, language translation, and chatbot development.
- Annotated Imagery Data: Essential for computer vision applications such as image recognition, object detection, and facial recognition.
- Synthetic Data: Generated data that mimics real-world data, useful for training models when real data is scarce or privacy concerns are paramount.
- Audio Data: Used in speech recognition, voice assistants, and audio classification tasks.
How is the Machine Learning (ML) Training Data Collected?
Datasets for Machine Learning can be sourced from:
- Commercial Data Providers: Companies that sell specialized datasets. Datarade offers high-quality, curated datasets from reputable providers, ensuring data quality for specific ML applications.
- Public Databases: Open-access repositories and government portals offer datasets that are suitable for some use cases, such as study or university projects.
- Generated Data: Synthetic data can be created to simulate real-world scenarios.
- Internal Data: Data collected and maintained by organizations from their own operations, customers, and processes.
How to Train an Machine Learning Model with Data?
Training a Machine Learning model with data involves 7 steps:
- Data Collection: Gather relevant data from various sources.
- Data Preprocessing: Clean and prepare the data, handling missing values and normalizing features.
- Feature Selection: Identify the most important features that influence the target variable.
- Model Selection: Choose an appropriate algorithm for the task.
- Training: Use the training data to teach the model to recognize patterns.
- Evaluation: Assess the performance using test data.
- Tuning: Adjust model parameters to improve accuracy.
Frequently Asked Questions
How is the Quality of Machine Learning (ML) Data Maintained?
The quality of Machine Learning (ML) Data is ensured through rigorous validation processes, such as cross-referencing with reliable sources, monitoring accuracy rates, and filtering out inconsistencies. High-quality datasets often report match rates, regular updates, and adherence to industry standards.
How Frequently is Machine Learning (ML) Data Updated?
The update frequency for Machine Learning (ML) Data varies by provider and dataset. Some datasets are refreshed daily or weekly, while others update less frequently. When evaluating options, ensure you select a dataset with a frequency that suits your specific use case.
Is Machine Learning (ML) Data Secure?
The security of Machine Learning (ML) Data is prioritized through compliance with industry standards, including encryption, anonymization, and secure delivery methods like SFTP and APIs. At Datarade, we enforce strict policies, requiring all our providers to adhere to regulations such as GDPR, CCPA, and other relevant data protection standards.
How is Machine Learning (ML) Data Delivered?
Machine Learning (ML) Data can be delivered in formats such as CSV, JSON, XML, or via APIs, enabling seamless integration into your systems. Delivery frequencies range from real-time updates to scheduled intervals (daily, weekly, monthly, or on-demand). Choose datasets that align with your preferred delivery method and system compatibility for Machine Learning (ML) Data.
How Much Does Machine Learning (ML) Data Cost?
The cost of Machine Learning (ML) Data depends on factors like the datasets size, scope, update frequency, and customization level. Pricing models may include one-off purchases, monthly or yearly subscriptions, or usage-based fees. Many providers offer free samples, allowing you to evaluate the suitability of Machine Learning (ML) Data for your needs.
What Are Similar Data Types to Machine Learning (ML) Data?
Machine Learning (ML) Data is similar to other data types, such as Annotated Imagery Data, Deep Learning (DL) Data, Synthetic Data, Textual data, and Audio Data. These related categories are often used together for applications like Artificial Intelligence (AI) and Deep Learning.
